基于视觉-语言模型的可微射线追踪：用于快速且准确的多材料射频参数估计
Vision-Language-Model-Guided Differentiable Ray Tracing for Fast and Accurate Multi-Material RF Parameter Estimation
AI总结：
本文提出一种基于视觉-语言模型的可微射线追踪框架，通过解析场景图像推断材料类别并指导射频参数估计，实现更快的收敛速度和更低的参数误差。
摘要：
准确的射频（RF）材料参数对于6G系统中的电磁数字孪生至关重要，然而基于梯度的逆射线追踪（RT）在初始化敏感且在有限测量下成本高昂。本文提出了一种视觉-语言模型（VLM）引导的框架，该框架在可微射线追踪（DRT）引擎中加速并稳定了多材料参数估计。VLM解析场景图像以推断材料类别，并通过ITU-R材料表将其映射到定量先验，从而获得有指导性的导电率初始化。VLM进一步选择具有信息量的发射器/接收器位置，以促进多样且材料可区分的路径。从这些先验开始，DRT通过测量接收到的信号强度进行基于梯度的细化。在NVIDIA Sionna上对室内场景的实验表明，与均匀或随机初始化和随机放置基线相比，收敛速度提高了2-4倍，最终参数误差降低了10-100倍，仅使用少量接收器即可实现平均相对误差低于0.1%。复杂性分析表明，每迭代时间与材料数量和测量设置数量呈近线性关系，而VLM引导的放置减少了准确恢复所需的测量次数。对RT深度和射线数量的消融实验进一步确认了在不显著增加每迭代开销的情况下获得进一步的准确性提升。结果表明，来自VLM的语义先验有效指导了基于物理的优化，以实现快速且可靠的射频材料估计。
英文摘要：
Accurate radio-frequency (RF) material parameters are essential for electromagnetic digital twins in 6G systems, yet gradient-based inverse ray tracing (RT) remains sensitive to initialization and costly under limited measurements. This paper proposes a vision-language-model (VLM) guided framework that accelerates and stabilizes multi-material parameter estimation in a differentiable RT (DRT) engine. A VLM parses scene images to infer material categories and maps them to quantitative priors via an ITU-R material table, yielding informed conductivity initializations. The VLM further selects informative transmitter/receiver placements that promote diverse, material-discriminative paths. Starting from these priors, the DRT performs gradient-based refinement using measured received signal strengths. Experiments in NVIDIA Sionna on indoor scenes show 2-4$\times$ faster convergence and 10-100$\times$ lower final parameter error compared with uniform or random initialization and random placement baselines, achieving sub-0.1\% mean relative error with only a few receivers. Complexity analyses indicate per-iteration time scales near-linearly with the number of materials and measurement setups, while VLM-guided placement reduces the measurements required for accurate recovery. Ablations over RT depth and ray counts confirm further accuracy gains without significant per-iteration overhead. Results demonstrate that semantic priors from VLMs effectively guide physics-based optimization for fast and reliable RF material estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
